1. Discussion on using Evapotranspiration for Water Rights Management
Rick Allen -- University of Idaho, Kimberly, Idaho
Partners and Collaborators:
Jeppe Kjaersgaard, Magali Garcia, R. Trezza – University of Idaho
Tony Morse, W. Kramber – Idaho Dept. Water Resources
Wim Bastiaanssen – WaterWatch, M. Tasumi --Univ. Miyazaki, Japan
James Wright -- USDA-ARS

2. (radiation from sun and sky)
METRIC Energy balance
ET is calculated as a “residual” of the energy balance R n
(radiation from sun and sky) ET
H (heat to air)
ET = R - G - H n
Basic Truth: Evaporation consumes Energy
The energy balance includes all major sources (Rn) and consumers (ET, G, H) of energy
G (heat to ground)

3. Energy balance gives us “actual” ET
Therefore, we can account for impacts on ET caused by:
water shortage
disease
crop variety
planting density
cropping dates
salinity
management
(these effects can be converted into a crop coefficient)

4. Interpolation of ETrF (i.e., Kc) for Monthly or Seasonal ET
0.2
0.4
0.6
0.8 1
1.2
3/1
4/1
5/1
6/1
7/1
8/1
9/1
10/1
11/1
ETrF
Splined
Satellite Date
Corn 2000

5. Lysimeter at Kimberly (Wright)
Comparison with Lysimeter Measurements:
Lysimeter at Kimberly (Wright)
12/17/01

6. Lysimeter data by Dr. J.L. Wright, USDA-ARS
Kimberly, Idaho – Periods between Satellites
METRIC ET for period
Sugar Beets
Sugar Beets, 1989
Kimberly, Idaho
Period of Partial Cover
Lysimeter data by Dr. J.L. Wright, USDA-ARS

7. Seasonal ET

8. Comparison of Seasonal ET by METRICtm with Lysimeter
ET (mm) - April-Sept., Kimberly, 1989
Sugar Beets
METRIC
METRIC
714 mm
Lysimeter 718 mm

9. Comparison of Seasonal ET by SEBAL2000 with Lysimeter
ET (mm) - July-Oct., Montpelier, ID 1985
Lysimeter 388 mm
SEBAL 405 mm

10. Sharpening of Landsat 5 Thermal Band to 30 m
ETrF
July
2006
Temp.
original (120 m thermal)
sharpened (30 m thermal)

11. Sharpening of Landsat 5 Thermal Band to 30 m
Growing Season, 2006 – ET aggregated inside CLU’s

12. Comparison to Kc Curves

13. 717 fields in the Twin Falls area
METRIC applied to year 2000
717 fields in the Twin Falls area
Average “curve”
Vegetation Index

14. Kc near 1.0 indicating high production agriculture
516 fields

15. 564 fields

16. 325 fields

17. Approaches – 1 (METRIC) Base ET estimates on METRIC
“in-season injury assessment”
Approaches – 1 (METRIC)
Base ET estimates on METRIC
--7 to 10 day lag time, high expense
--can apply an ‘attainable’ efficiency to derive Diversion requirement
Can normalize to NDVI to estimate stress
Can compare with actual Diversions, ET/NDVI from a few other years (2000, 2003, 2006)
Advantage – gives ‘actual’ ET
Disadvantage
Expensive and with time delay
One ‘look’ each 16 days only, at best
Some native uncertainty in ET estimates (+/-10%?)

18. Approaches – 2 (Satellite NDVI)
“in-season injury assessment”
Approaches – 2 (Satellite NDVI)
Base ET estimates on NDVI
--quick, one day lag time, low expense
--apply an ‘attainable’ efficiency to derive Diversion requirement
Compare with actual Diversions
Advantage
quick, low cost
can use SPOT, IRS, etc. if the current LS fails
Disadvantage
May not see ET reductions caused by stress (water shortage)
“Injury” based on act. vs. required diversions

19. Approaches – 3 (no satellite)
“in-season injury assessment”
Approaches – 3 (no satellite)
Calculate ratio of running average Diversion to running average reference ET (from weather data)
Compare to other years (> 20)
Advantage
quick, inexpensive
longer time series for context (>20 years for Agrimet)
Disadvantage
May need to normalize for cropping patterns
May need to normalize for shift to sprinklers

20. “basal” Kc
“mean” Kc
“mean” Kc
“basal” Kc

21. “mean” Kc
“mean” Kc
“basal” Kc

22. “mean” Kc vs. NDVI
Well-watered fields
Magic Valley, 2000
Kcm

23. “mean” Kc vs. NDVI
Well-watered fields

24. Development of a seasonal Kc curve from NDVI – Comparison against 1989 Lysimeter data at Kimberly for Landsat Overpass Dates (Kc and NDVI were then splined between dates to obtain daily ET estimates)

25. Comparisons between daily ET determined by METRIC for specific crops and ET determined from the general Kcm vs. NDVIsurf relationship, year 2000, Magic Valley, averaged over 100’s of sampled fields

26. Comparisons between 5-day ET determined by METRIC for specific crops and ET determined from the general Kcm vs. NDVIsurf relationship, , year 2000, Magic Valley, averaged over 100’s of sampled fields

27. Error (%) in seasonal ET estimated using Kc estimated using the NDVI (normalized difference vegetation index) relative to seasonal ET calculated by METRIC – positive values indicate overestimation.

28. “Performance” of Irrigation Projects

29. Twin Falls Tract -- 220,000 acres -- Alfalfa Reference Basis
Irrigation Project Performance -- Idaho
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Project wide Crop Coefficient -- METRIC
Twin Falls Tract ,000 acres -- Alfalfa Reference Basis
2000
2003 Kc
March, Sept., and Oct. unavailable for 2003 due to clouds

30. Irrigation Project Performance -- Idaho
Twin Falls Canal Company, Idaho

31. Can the NDVI-based Kc pick up ‘stress’ caused by water shortage?
“mean” Kc
“basal” Kc
“mean” Kc
“basal” Kc
Stress? or
Random error
in Kc estimate?

35. High because of evaporation from surface flooding
or high because of no stress??

36. Issues If NDVI (and thus ET) is ‘low’ is it because:
shift in crop types due to market
shift in crop types because of perceived water shortage (i.e., internal mitigation)
chronic shortage of water during development
cool spring – late/retarded development
warm summer – accelerated ripening

37. Approaches – 1 (METRIC) Base ET estimates on METRIC
“in-season injury assessment”
Approaches – 1 (METRIC)
Base ET estimates on METRIC
--7 to 10 day lag time, high expense
--can apply an ‘attainable’ efficiency to derive Diversion requirement
Can normalize to NDVI to estimate stress
Can compare with actual Diversions, ET/NDVI from a few other years (2000, 2003, 2006)
Advantage – gives ‘actual’ ET
Disadvantage
Expensive and with time delay
One ‘look’ each 16 days only, at best
Some native uncertainty in ET estimates (+/-10%?)

38. Approaches – 2 (Satellite NDVI)
“in-season injury assessment”
Approaches – 2 (Satellite NDVI)
Base ET estimates on NDVI
--quick, one day lag time, low expense
--apply an ‘attainable’ efficiency to derive Diversion requirement
Compare with actual Diversions
Advantage
quick, low cost
can use SPOT, IRS, etc. if the current LS fails
Disadvantage
May not see ET reductions caused by stress (water shortage)
“Injury” based on act. vs. required diversions

39. Approaches – 3 (no satellite)
“in-season injury assessment”
Approaches – 3 (no satellite)
Calculate ratio of running average Diversion to running average reference ET (from weather data)
Compare to other years (> 20)
Advantage
quick, inexpensive
longer time series for context (>20 years for Agrimet)
Disadvantage
May need to normalize for cropping patterns
May need to normalize for shift to sprinklers

40. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003
METRIC Analyses by Lorite, Allen and Robison

41. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003
METRIC Analyses by Lorite, Allen and Robison

42. Impact of Irrigation System Type on ET -- south-central Idaho -- 2003
METRIC Analyses by Lorite, Allen and Robison